Mathematical modeling and computational prediction of cancer drug resistance

Abstract

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.

Dissecting intratumoral myeloid cell plasticity by single cell RNA‐seq

“…Specifically, T‐cell mediated IL‐4 production following CSF1R inhibition induces macrophage‐mediated secretion of IGF‐1 that in turn activates intratumoral PI3K signaling (via glioma‐expressed IGF‐1 receptor) and the subsequent survival and growth of glioma cells . Furthermore, this mechanism of drug resistance was subsequently validated and quantitatively described through spatio‐temporal mathematical modeling using input from molecular networks involved in myeloid‐glioma crosstalk signaling . Our findings shed new light on the molecular nodes and signaling processes that underlie monocytes‐to‐M2 differentiation and myeloid‐tumor crosstalk, and provides a better understanding of myeloid‐mediated drug resistance mechanisms and facilitates the design of future immunotherapy strategies.…”

Targeting MAPK Signaling in Cancer: Mechanisms of Drug Resistance and Sensitivity

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Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

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